gemseo / problems / scalable / parametric

scalable_problem module¶

The scalable problem.

class gemseo.problems.scalable.parametric.scalable_problem.ScalableProblem(discipline_settings=(ScalableDisciplineSettings(d_i=1, p_i=1), ScalableDisciplineSettings(d_i=1, p_i=1)), d_0=1, add_random_variables=False, alpha=0.5, seed=1)[source]¶

Bases: ScalableProblem

The scalable problem.

It builds a set of strongly coupled scalable disciplines completed by a system discipline computing the objective function and the constraints.

These disciplines are defined on an unit design space, i.e. design variables belongs to \([0, 1]\).

Parameters:

discipline_settings (Sequence[ScalableDisciplineSettings]) –
The configurations of the different scalable disciplines.

By default it is set to (ScalableDisciplineSettings(d_i=1, p_i=1), ScalableDisciplineSettings(d_i=1, p_i=1)).
d_0 (int) –
The size of the shared design variable \(x_0\).

By default it is set to 1.
add_random_variables (bool) –
Whether to add a centered random variable \(u_i\) on the output of the \(i\)-th scalable discipline.

By default it is set to False.
alpha (float) –
The proportion of feasible design points.

By default it is set to 0.5.
seed (int) –
The seed for reproducibility.

By default it is set to 1.

compute_y(x)[source]¶

Compute the coupling vector \(y\).

Parameters:: x (ndarray[Any, dtype[float]]) – A design point.
Returns:: The coupling vector associated with the design point \(x\).
Return type:: ndarray[Any, dtype[float]]

create_quadratic_programming_problem(add_coupling=False)[source]¶

Create the quadratic programming (QP) version of the MDO problem.

This is an optimization problem to minimize \(0.5x^TQx + c^Tx + d\) with respect to \(x\) under the linear constraints \(Ax-b\leq 0\), where the matrix \(Q\) is symmetric.

Parameters:

add_coupling (bool) –

Whether to add the coupling variables as an observable.

By default it is set to False.

Returns:

The quadratic optimization problem.

Return type:

OptimizationProblem

create_scenario(use_optimizer=True, formulation_name='MDF', **formulation_options)[source]¶

Create the DOE or MDO scenario associated with this scalable problem.

Parameters:

use_optimizer (bool) –
Whether to use an optimizer or a design of experiments.

By default it is set to True.
formulation_name (str) –
The name of the formulation.

By default it is set to “MDF”.
**formulation_options (Any) – The options of the formulation.

Returns:

The scenario to be executed.

Return type:

Scenario

design_space: _DESIGN_SPACE_CLASS¶: The design space.

disciplines: list[_MAIN_DISCIPLINE_CLASS | _SCALABLE_DISCIPLINE_CLASS]¶: The disciplines.

property main_discipline: _MAIN_DISCIPLINE_CLASS¶: The main discipline.

qp_problem: QuadraticProgrammingProblem¶: The quadratic programming problem.

property scalable_disciplines: list[_SCALABLE_DISCIPLINE_CLASS]¶: The scalable disciplines.

Examples using ScalableProblem¶

Parametric scalable MDO problem - MDF